class_docstr: Encoder class for transaction data in Python lists
  
  Parameters
  ------------
  None
  
  Attributes
  ------------
  columns_: list
    List of unique names in the `X` input list of lists
  
  Examples
  ------------
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/preprocessing/TransactionEncoder/
docstr: Learn unique column names from transaction DataFrame
  
  Parameters
  ------------
  X : list of lists
    A python list of lists, where the outer list stores the
    n transactions and the inner list stores the items in each
    transaction.
  
    For example,
    [['Apple', 'Beer', 'Rice', 'Chicken'],
     ['Apple', 'Beer', 'Rice'],
     ['Apple', 'Beer'],
     ['Apple', 'Bananas'],
     ['Milk', 'Beer', 'Rice', 'Chicken'],
     ['Milk', 'Beer', 'Rice'],
     ['Milk', 'Beer'],
     ['Apple', 'Bananas']]
docstr_tok: ["Learn","unique","column","names","from","transaction","DataFrame","Parameters","--","--","--","--","--","--","X",":","list","of","lists","A","python","list","of","lists",",","where","the","outer","list","stores","the","n","transactions","and","the","inner","list","stores","the","items","in","each","transaction",".","For","example",",","[","[","'Apple","'",",","'Beer","'",",","'Rice","'",",","'Chicken","'","]",",","[","'Apple","'",",","'Beer","'",",","'Rice","'","]",",","[","'Apple","'",",","'Beer","'","]",",","[","'Apple","'",",","'Bananas","'","]",",","[","'Milk","'",",","'Beer","'",",","'Rice","'",",","'Chicken","'","]",",","[","'Milk","'",",","'Beer","'",",","'Rice","'","]",",","[","'Milk","'",",","'Beer","'","]",",","[","'Apple","'",",","'Bananas","'","]","]"]
code: def fit(self, X):
      """Learn unique column names from transaction DataFrame
  
      Parameters
      ------------
      X : list of lists
        A python list of lists, where the outer list stores the
        n transactions and the inner list stores the items in each
        transaction.
  
        For example,
        [['Apple', 'Beer', 'Rice', 'Chicken'],
         ['Apple', 'Beer', 'Rice'],
         ['Apple', 'Beer'],
         ['Apple', 'Bananas'],
         ['Milk', 'Beer', 'Rice', 'Chicken'],
         ['Milk', 'Beer', 'Rice'],
         ['Milk', 'Beer'],
         ['Apple', 'Bananas']]
  
      """
      unique_items = set()
      for transaction in X:
          for item in transaction:
              unique_items.add(item)
      self.columns_ = sorted(unique_items)
      columns_mapping = {}
      for col_idx, item in enumerate(self.columns_):
          columns_mapping[item] = col_idx
      self.columns_mapping_ = columns_mapping
      return self
  
file_path: mlxtend/mlxtend/preprocessing/transactionencoder.py
imports: import numpy
  from scipy.sparse import csr_matrix
  from sklearn.base import BaseEstimator, TransformerMixin
label_desc: Use image node_id 2 for calling the TransactionEncoder obj's underlying member method code with example usage: obj.fit(X) and returns: self